Main Effect
Analysis
fairness
m1 <- lme4::glmer(fairness ~ nonwhite_share*incomeShare_difference+ (1 | X), cd, family = binomial(link = "logit"))
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: fairness ~ nonwhite_share * incomeShare_difference + (1 | X)
## Data: cd
##
## AIC BIC logLik deviance df.resid
## 646.1 667.1 -318.0 636.1 490
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.0672 -0.7831 -0.6099 1.0962 1.9953
##
## Random effects:
## Groups Name Variance Std.Dev.
## X (Intercept) 0 0
## Number of obs: 495, groups: X, 495
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2956864 0.2198051 -1.345 0.1786
## nonwhite_share -0.0096816 0.0084052 -1.152 0.2494
## incomeShare_difference -0.3159842 0.1862136 -1.697 0.0897 .
## nonwhite_share:incomeShare_difference 0.0009131 0.0064086 0.142 0.8867
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) nnwht_ incmS_
## nonwhit_shr -0.902
## incmShr_dff 0.039 -0.035
## nnwht_sh:S_ -0.042 0.053 -0.922
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
plot
fairness_plot <- interactions::interact_plot(m1, pred = nonwhite_share, modx = incomeShare_difference, x.label = "Non white Ratio", y.label = "0 = fair, 1 = unfair", legend.main = "income share difference") + ylim(c(0,1))
fairness_plot_maineffect <- annotate_figure(fairness_plot,
top = text_grob("Fairness by Condition", color = "black", face = "bold", size = 14))
ggsave(plot = fairness_plot_maineffect, width = 8, height = 6, filename = "fairness_plot_maineffect.jpg")
knitr::include_graphics("fairness_plot_maineffect.jpg")

team work
m2 <- lme4::glmer(team_work ~ nonwhite_share*incomeShare_difference + (1 | X), cd, family = binomial(link = "logit"))
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: team_work ~ nonwhite_share * incomeShare_difference + (1 | X)
## Data: cd
##
## AIC BIC logLik deviance df.resid
## 533.9 554.9 -261.9 523.9 490
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.8463 -0.5643 -0.4788 -0.4271 2.4432
##
## Random effects:
## Groups Name Variance Std.Dev.
## X (Intercept) 1.603e-14 1.266e-07
## Number of obs: 495, groups: X, 495
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.481754 0.260631 -5.685 1.31e-08 ***
## nonwhite_share 0.010539 0.009656 1.091 0.275
## incomeShare_difference -0.100560 0.218254 -0.461 0.645
## nonwhite_share:incomeShare_difference -0.004750 0.007323 -0.649 0.517
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) nnwht_ incmS_
## nonwhit_shr -0.908
## incmShr_dff 0.082 -0.076
## nnwht_sh:S_ -0.087 0.105 -0.929
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
plot
team_work_plot <- interactions::interact_plot(m2, pred = nonwhite_share, modx = incomeShare_difference, x.label = "Non white Ratio", y.label = "0 = team work well, 1 = team work not well", legend.main = "income share difference") + ylim(c(0,1))
team_work_plot_maineffect <- annotate_figure(team_work_plot,
top = text_grob("Team Work by Condition", color = "black", face = "bold", size = 14))
ggsave(plot = team_work_plot_maineffect, width = 8, height = 6, filename = "team_work_plot_maineffect.jpg")
knitr::include_graphics("team_work_plot_maineffect.jpg")

work in team
m3 <- lme4::glmer(work_in_team ~ nonwhite_share*incomeShare_difference + (1 | X), cd, family = binomial(link = "logit"))
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: work_in_team ~ nonwhite_share * incomeShare_difference + (1 | X)
## Data: cd
##
## AIC BIC logLik deviance df.resid
## 668.4 689.4 -329.2 658.4 490
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3333 -0.8906 -0.6649 0.9903 1.6359
##
## Random effects:
## Groups Name Variance Std.Dev.
## X (Intercept) 4.648e-14 2.156e-07
## Number of obs: 495, groups: X, 495
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2498162 0.2164024 -1.154 0.2483
## nonwhite_share 0.0011320 0.0081868 0.138 0.8900
## incomeShare_difference -0.3375464 0.1838670 -1.836 0.0664 .
## nonwhite_share:incomeShare_difference 0.0006399 0.0062831 0.102 0.9189
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) nnwht_ incmS_
## nonwhit_shr -0.904
## incmShr_dff 0.005 0.001
## nnwht_sh:S_ -0.002 -0.001 -0.924
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
plot
work_in_team_plot <- interactions::interact_plot(m3, pred = nonwhite_share, modx = incomeShare_difference, x.label = "Non white Ratio", y.label = "0 = want to work in team, 1 = do not want to work in team", legend.main = "income share difference") + ylim(c(0,1))
work_in_team_plot_maineffect_nw <- annotate_figure(work_in_team_plot,
top = text_grob("Work in Team by Condition", color = "black", face = "bold", size = 14))
ggsave(plot = work_in_team_plot_maineffect_nw, width = 8, height = 6, filename = "work_in_team_maineffect_nw.jpg")
knitr::include_graphics("work_in_team_maineffect_nw.jpg")

distrust
m4 <- lme4::glmer(distrust ~ nonwhite_share*incomeShare_difference + (1 | X), cd, family = binomial(link = "logit"))
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: distrust ~ nonwhite_share * incomeShare_difference + (1 | X)
## Data: cd
##
## AIC BIC logLik deviance df.resid
## 645.8 666.9 -317.9 635.8 490
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.0789 -0.7822 -0.6089 1.0529 1.9178
##
## Random effects:
## Groups Name Variance Std.Dev.
## X (Intercept) 0 0
## Number of obs: 495, groups: X, 495
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.435225 0.221298 -1.967 0.0492 *
## nonwhite_share -0.003500 0.008399 -0.417 0.6768
## incomeShare_difference -0.349539 0.187804 -1.861 0.0627 .
## nonwhite_share:incomeShare_difference 0.001468 0.006424 0.228 0.8193
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) nnwht_ incmS_
## nonwhit_shr -0.903
## incmShr_dff 0.052 -0.043
## nnwht_sh:S_ -0.050 0.056 -0.924
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
plot
distrust_plot_nw <- interactions::interact_plot(m4, pred = nonwhite_share, modx = incomeShare_difference, x.label = "Non white Ratio", y.label = "0 = trust , 1 = distrust", legend.main = "income share difference") + ylim(c(0,1))
distrust_maineffect_nw <- annotate_figure(distrust_plot_nw,
top = text_grob("Trust by Condition", color = "black", face = "bold", size = 14))
ggsave(plot = distrust_maineffect_nw, width = 10, height = 8, filename = "distrust_maineffect_nw.jpg")
knitr::include_graphics("distrust_maineffect_nw.jpg")

Table with
Results
M1 = Fairness NW (DV), M2 = Team Work NW (DV), M3 = Want to work in team
NW (DV), M4 = Distrust NW (DV)
|
Â
|
Model 1
|
Model 2
|
Model 3
|
Model 4
|
|
(Intercept)
|
-.296
|
-1.482***
|
-.250
|
-.435*
|
|
Â
|
(.220)
|
(.261)
|
(.216)
|
(.221)
|
|
nonwhite_share
|
-.010
|
.011
|
.001
|
-.004
|
|
Â
|
(.008)
|
(.010)
|
(.008)
|
(.008)
|
|
incomeShare_difference
|
-.316
|
-.101
|
-.338
|
-.350
|
|
Â
|
(.186)
|
(.218)
|
(.184)
|
(.188)
|
|
nonwhite_share:incomeShare_difference
|
.001
|
-.005
|
.001
|
.001
|
|
Â
|
(.006)
|
(.007)
|
(.006)
|
(.006)
|
|
AIC
|
646.058
|
533.855
|
668.406
|
645.838
|
|
BIC
|
667.080
|
554.878
|
689.429
|
666.861
|
|
Log Likelihood
|
-318.029
|
-261.928
|
-329.203
|
-317.919
|
|
Num. obs.
|
495
|
495
|
495
|
495
|
|
Num. groups: X
|
495
|
495
|
495
|
495
|
|
Var: X (Intercept)
|
.000
|
.000
|
.000
|
.000
|
|
***p < 0.001; **p < 0.01; *p <
0.05
|
fairness_nw_w <- ggarrange(fairness_plot_maineffect_w,fairness_plot_maineffect, ncol = 2, nrow = 2, common.legend = T)
ggsave(plot = fairness_nw_w, width = 10, height = 10, filename = "fairness_nw_w.jpg")
knitr::include_graphics("fairness_nw_w.jpg")

want_to_work_in_team_nw_w <- ggarrange(work_in_team_plot_maineffect_w,work_in_team_plot_maineffect_nw, ncol = 2, nrow = 2, common.legend = T)
ggsave(plot = want_to_work_in_team_nw_w, width = 10, height = 10, filename = "want_to_work_in_team_nw_w.jpg")
knitr::include_graphics("want_to_work_in_team_nw_w.jpg")

distrust_nw_w <- ggarrange(distrust_maineffect_w,distrust_maineffect_nw, ncol = 2, nrow = 2, common.legend = T)
ggsave(plot = distrust_nw_w, width = 10, height = 10, filename = "distrust_nw_w.jpg")
knitr::include_graphics("distrust_nw_w.jpg")

Main Effects Analysis
with control variables
fairness
m9 <- lme4::glmer(fairness ~ nonwhite_share*incomeShare_difference + age + gender + ethnic + edu + marital + children + employment + income_log + ladder + PoliticalOrientation+(1 | X), cd, family = binomial(link = "logit"))
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: fairness ~ nonwhite_share * incomeShare_difference + age + gender +
## ethnic + edu + marital + children + employment + income_log +
## ladder + PoliticalOrientation + (1 | X)
## Data: cd
##
## AIC BIC logLik deviance df.resid
## 649.8 742.3 -302.9 605.8 473
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7898 -0.7696 -0.5067 1.0201 2.8114
##
## Random effects:
## Groups Name Variance Std.Dev.
## X (Intercept) 1.681e-14 1.297e-07
## Number of obs: 495, groups: X, 495
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.7345805 1.8083780 2.618 0.00884 **
## nonwhite_share -0.0102955 0.0086708 -1.187 0.23508
## incomeShare_difference -0.3036798 0.1952170 -1.556 0.11980
## age -0.0015560 0.0109295 -0.142 0.88679
## gendero 0.4666909 1.0376610 0.450 0.65289
## ethnic1 -0.5578602 0.2428922 -2.297 0.02163 *
## edu2 0.7423404 0.8648853 0.858 0.39072
## edu3 0.3457048 0.8597208 0.402 0.68760
## edu4 0.1758548 0.9092082 0.193 0.84663
## edu5 0.4771796 0.8782519 0.543 0.58690
## edu6 0.4390157 0.9236484 0.475 0.63457
## marital3 -0.8615496 0.9713808 -0.887 0.37512
## marital4 -0.0923857 0.3733415 -0.247 0.80456
## children -0.0349484 0.1798085 -0.194 0.84589
## employmentFull-time 0.1690527 0.2818878 0.600 0.54870
## employmentPrefer not to answer 0.2601685 0.3209552 0.811 0.41759
## employmentUnemployed 1.4241994 1.0400381 1.369 0.17088
## income_log -0.3766638 0.1302141 -2.893 0.00382 **
## ladder -0.2060091 0.0730639 -2.820 0.00481 **
## PoliticalOrientation -0.0554099 0.0701608 -0.790 0.42967
## nonwhite_share:incomeShare_difference 0.0004449 0.0067254 0.066 0.94725
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 21 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
plot
knitr::include_graphics("fairness_maineffect_cov_nw.jpg")

work in team
m10 <- lme4::glmer(work_in_team ~ nonwhite_share*incomeShare_difference + age + gender + ethnic + edu + marital + children + employment + income_log + ladder + PoliticalOrientation+(1 | X), cd, family = binomial(link = "logit"))
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: work_in_team ~ nonwhite_share * incomeShare_difference + age +
## gender + ethnic + edu + marital + children + employment +
## income_log + ladder + PoliticalOrientation + (1 | X)
## Data: cd
##
## AIC BIC logLik deviance df.resid
## 667.3 759.8 -311.7 623.3 473
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6341 -0.8372 -0.5123 0.9521 2.1623
##
## Random effects:
## Groups Name Variance Std.Dev.
## X (Intercept) 0.01502 0.1226
## Number of obs: 495, groups: X, 495
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 5.198541 1.850658 2.809 0.00497 **
## nonwhite_share 0.001362 0.008511 0.160 0.87283
## incomeShare_difference -0.418551 0.198497 -2.109 0.03498 *
## age -0.031737 0.011764 -2.698 0.00698 **
## gendero 1.040216 1.047798 0.993 0.32083
## ethnic1 -0.220672 0.232198 -0.950 0.34193
## edu2 0.671572 0.875675 0.767 0.44313
## edu3 0.381170 0.868775 0.439 0.66085
## edu4 -0.204959 0.919871 -0.223 0.82368
## edu5 0.526651 0.885612 0.595 0.55206
## edu6 0.461576 0.930834 0.496 0.61998
## marital3 -0.881549 0.978847 -0.901 0.36780
## marital4 -0.424068 0.377687 -1.123 0.26152
## children 0.035910 0.173659 0.207 0.83618
## employmentFull-time 0.202743 0.274360 0.739 0.45993
## employmentPrefer not to answer 0.583755 0.317078 1.841 0.06561 .
## employmentUnemployed -1.013243 1.226937 -0.826 0.40890
## income_log -0.345703 0.132526 -2.609 0.00909 **
## ladder -0.189897 0.073450 -2.585 0.00973 **
## PoliticalOrientation -0.007694 0.068161 -0.113 0.91012
## nonwhite_share:incomeShare_difference 0.002035 0.006694 0.304 0.76114
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 21 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0147303 (tol = 0.002, component 1)
## Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?
plot
work_in_team_maineffect <- interactions::interact_plot(m10, pred = nonwhite_share, modx = incomeShare_difference, x.label = "income share held by non white employees", y.label = "0 = want to work in team, 1 = do not want to work in team", legend.main = "income share difference") + ylim(c(0,1))
work_in_team_maineffect_annotated <- annotate_figure(work_in_team_maineffect,
top = text_grob("Work in Team by Condition", color = "black", face = "bold", size = 14))
ggsave(plot = work_in_team_maineffect_annotated, width = 8, height = 6, filename = "work_in_team_cov_nw.jpg")
knitr::include_graphics("work_in_team_cov_nw.jpg")

team work
m11 <- lme4::glmer(team_work ~ nonwhite_share*incomeShare_difference + age + gender + ethnic + edu + marital + children + employment + income_log + ladder + PoliticalOrientation+(1 | X), cd, family = binomial(link = "logit"))
## Warning in vcov.merMod(object, use.hessian = use.hessian): variance-covariance matrix computed from finite-difference Hessian is
## not positive definite or contains NA values: falling back to var-cov estimated from RX
## Warning in vcov.merMod(object, correlation = correlation, sigm = sig): variance-covariance matrix computed from finite-difference Hessian is
## not positive definite or contains NA values: falling back to var-cov estimated from RX
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: team_work ~ nonwhite_share * incomeShare_difference + age + gender +
## ethnic + edu + marital + children + employment + income_log +
## ladder + PoliticalOrientation + (1 | X)
## Data: cd
##
## AIC BIC logLik deviance df.resid
## 529.1 621.6 -242.5 485.1 473
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.1043 -0.5913 -0.4166 -0.0799 4.8883
##
## Random effects:
## Groups Name Variance Std.Dev.
## X (Intercept) 1.026e-08 0.0001013
## Number of obs: 495, groups: X, 495
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.272e+00 2.028e+00 2.106 0.035194
## nonwhite_share 1.140e-02 1.003e-02 1.137 0.255662
## incomeShare_difference -8.113e-02 2.319e-01 -0.350 0.726470
## age 5.824e-03 1.261e-02 0.462 0.644287
## gendero -2.004e+01 3.074e+04 -0.001 0.999480
## ethnic1 1.011e-01 2.697e-01 0.375 0.707755
## edu2 8.731e-01 1.025e+00 0.852 0.394223
## edu3 3.659e-01 1.011e+00 0.362 0.717356
## edu4 1.848e-01 1.065e+00 0.174 0.862243
## edu5 4.167e-01 1.041e+00 0.400 0.688989
## edu6 4.095e-02 1.086e+00 0.038 0.969924
## marital3 3.891e-01 1.074e+00 0.362 0.716989
## marital4 2.742e-01 4.298e-01 0.638 0.523464
## children 5.857e-02 2.190e-01 0.267 0.789171
## employmentFull-time 1.103e+00 3.565e-01 3.095 0.001970
## employmentPrefer not to answer 4.013e-01 4.022e-01 0.998 0.318333
## employmentUnemployed -1.963e+01 2.746e+04 -0.001 0.999430
## income_log -5.481e-01 1.495e-01 -3.666 0.000246
## ladder -1.870e-01 8.504e-02 -2.200 0.027842
## PoliticalOrientation -1.311e-01 8.186e-02 -1.602 0.109225
## nonwhite_share:incomeShare_difference -4.490e-03 7.763e-03 -0.578 0.562991
##
## (Intercept) *
## nonwhite_share
## incomeShare_difference
## age
## gendero
## ethnic1
## edu2
## edu3
## edu4
## edu5
## edu6
## marital3
## marital4
## children
## employmentFull-time **
## employmentPrefer not to answer
## employmentUnemployed
## income_log ***
## ladder *
## PoliticalOrientation
## nonwhite_share:incomeShare_difference
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 21 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## unable to evaluate scaled gradient
## Hessian is numerically singular: parameters are not uniquely determined
plot
team_work_maineffect <- interactions::interact_plot(m11, pred = nonwhite_share, modx = incomeShare_difference, x.label = "income share held by non white employees", y.label = "0 = team work well, 1 = team work not well", legend.main = "income share difference") + ylim(c(0,1))
team_work_maineffect_annotated <- annotate_figure(team_work_maineffect,
top = text_grob("Team work by Condition", color = "black", face = "bold", size = 14))
ggsave(plot = work_in_team_maineffect_annotated, width = 8, height = 6, filename = "team_work_cov_nw.jpg")
knitr::include_graphics("team_work_cov_nw.jpg")

trust of the
organization’s leadership
m12 <- lme4::glmer(distrust ~ nonwhite_share*incomeShare_difference + age + gender + ethnic + edu + marital + children + employment + income_log + ladder + PoliticalOrientation+(1 | X), cd, family = binomial(link = "logit"))
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: distrust ~ nonwhite_share * incomeShare_difference + age + gender +
## ethnic + edu + marital + children + employment + income_log +
## ladder + PoliticalOrientation + (1 | X)
## Data: cd
##
## AIC BIC logLik deviance df.resid
## 634.6 727.1 -295.3 590.6 473
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6296 -0.7640 -0.4620 0.9492 3.3144
##
## Random effects:
## Groups Name Variance Std.Dev.
## X (Intercept) 0.02394 0.1547
## Number of obs: 495, groups: X, 495
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.573820 1.878309 2.435 0.014889 *
## nonwhite_share -0.003914 0.008844 -0.443 0.658061
## incomeShare_difference -0.417280 0.205253 -2.033 0.042052 *
## age -0.004376 0.011117 -0.394 0.693887
## gendero 0.851268 1.057760 0.805 0.420944
## ethnic1 -0.303996 0.243208 -1.250 0.211319
## edu2 0.991946 0.881202 1.126 0.260304
## edu3 0.824138 0.873154 0.944 0.345240
## edu4 -0.171886 0.927253 -0.185 0.852938
## edu5 0.594688 0.893761 0.665 0.505809
## edu6 0.851637 0.937909 0.908 0.363869
## marital3 -1.095424 0.987408 -1.109 0.267261
## marital4 0.263758 0.388035 0.680 0.496676
## children 0.157693 0.185482 0.850 0.395226
## employmentFull-time 0.912092 0.314953 2.896 0.003780 **
## employmentPrefer not to answer 1.249962 0.352988 3.541 0.000398 ***
## employmentUnemployed 0.921648 1.060033 0.869 0.384600
## income_log -0.483483 0.141503 -3.417 0.000634 ***
## ladder -0.202365 0.077575 -2.609 0.009090 **
## PoliticalOrientation -0.063682 0.071475 -0.891 0.372950
## nonwhite_share:incomeShare_difference 0.002740 0.006923 0.396 0.692219
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 21 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0287821 (tol = 0.002, component 1)
## Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?
plot
distrust_maineffect <- interactions::interact_plot(m12, pred = nonwhite_share, modx = incomeShare_difference, x.label = "income share held by non white employees", y.label = "0 = trust , 1 = distrust", legend.main = "income share difference") + ylim(c(0,1))
distrust_maineffect_annotated <- annotate_figure(distrust_maineffect,
top = text_grob("Trust by Condition", color = "black", face = "bold", size = 14))
ggsave(plot = distrust_maineffect_annotated, width = 8, height = 6, filename = "distrust_cov_nw.jpg")
knitr::include_graphics("distrust_cov_nw.jpg")

Table
M9 = Fairness nw (DV), M10 = Want to work in team nw(DV), M11 = Trust
org nw (DV), M12 = Distrust nw (DV), M13 = Fairness w (DV), M14 = Want
to work in team w (DV), M15 = Trust org w(DV), M16 = Distrust w (DV)
|
Â
|
Model 1
|
Model 2
|
Model 3
|
Model 4
|
Model 5
|
Model 6
|
Model 7
|
Model 8
|
|
(Intercept)
|
4.735**
|
5.199**
|
4.272*
|
4.574*
|
3.705
|
5.331**
|
5.412*
|
4.185*
|
|
Â
|
(1.808)
|
(1.851)
|
(2.028)
|
(1.878)
|
(1.898)
|
(1.949)
|
(2.165)
|
(1.970)
|
|
nonwhite_share
|
-.010
|
.001
|
.011
|
-.004
|
Â
|
Â
|
Â
|
Â
|
|
Â
|
(.009)
|
(.009)
|
(.010)
|
(.009)
|
Â
|
Â
|
Â
|
Â
|
|
incomeShare_difference
|
-.304
|
-.419*
|
-.081
|
-.417*
|
-.259
|
-.214
|
-.530
|
-.144
|
|
Â
|
(.195)
|
(.198)
|
(.232)
|
(.205)
|
(.498)
|
(.495)
|
(.567)
|
(.511)
|
|
age
|
-.002
|
-.032**
|
.006
|
-.004
|
-.002
|
-.032**
|
.006
|
-.004
|
|
Â
|
(.011)
|
(.012)
|
(.013)
|
(.011)
|
(.011)
|
(.012)
|
(.013)
|
(.011)
|
|
gendero
|
.467
|
1.040
|
-20.040
|
.851
|
.467
|
1.040
|
-19.905
|
.853
|
|
Â
|
(1.038)
|
(1.048)
|
(30738.135)
|
(1.058)
|
(1.038)
|
(1.048)
|
(512.000)
|
(1.057)
|
|
ethnic1
|
-.558*
|
-.221
|
.101
|
-.304
|
-.558*
|
-.221
|
.101
|
-.304
|
|
Â
|
(.243)
|
(.232)
|
(.270)
|
(.243)
|
(.243)
|
(.232)
|
(.270)
|
(.243)
|
|
edu2
|
.742
|
.672
|
.873
|
.992
|
.742
|
.674
|
.873
|
.990
|
|
Â
|
(.865)
|
(.876)
|
(1.025)
|
(.881)
|
(.865)
|
(.876)
|
(1.025)
|
(.881)
|
|
edu3
|
.346
|
.381
|
.366
|
.824
|
.346
|
.383
|
.366
|
.823
|
|
Â
|
(.860)
|
(.869)
|
(1.011)
|
(.873)
|
(.860)
|
(.869)
|
(1.011)
|
(.873)
|
|
edu4
|
.176
|
-.205
|
.185
|
-.172
|
.176
|
-.203
|
.185
|
-.172
|
|
Â
|
(.909)
|
(.920)
|
(1.065)
|
(.927)
|
(.909)
|
(.920)
|
(1.065)
|
(.927)
|
|
edu5
|
.477
|
.527
|
.417
|
.595
|
.477
|
.528
|
.417
|
.593
|
|
Â
|
(.878)
|
(.886)
|
(1.041)
|
(.894)
|
(.878)
|
(.886)
|
(1.041)
|
(.894)
|
|
edu6
|
.439
|
.462
|
.041
|
.852
|
.439
|
.464
|
.041
|
.850
|
|
Â
|
(.924)
|
(.931)
|
(1.086)
|
(.938)
|
(.924)
|
(.931)
|
(1.086)
|
(.938)
|
|
marital3
|
-.862
|
-.882
|
.389
|
-1.095
|
-.862
|
-.879
|
.389
|
-1.096
|
|
Â
|
(.971)
|
(.979)
|
(1.074)
|
(.987)
|
(.971)
|
(.979)
|
(1.073)
|
(.987)
|
|
marital4
|
-.092
|
-.424
|
.274
|
.264
|
-.092
|
-.424
|
.274
|
.265
|
|
Â
|
(.373)
|
(.378)
|
(.430)
|
(.388)
|
(.373)
|
(.378)
|
(.430)
|
(.388)
|
|
children
|
-.035
|
.036
|
.059
|
.158
|
-.035
|
.036
|
.059
|
.158
|
|
Â
|
(.180)
|
(.174)
|
(.219)
|
(.185)
|
(.180)
|
(.174)
|
(.219)
|
(.185)
|
|
employmentFull-time
|
.169
|
.203
|
1.103**
|
.912**
|
.169
|
.203
|
1.103**
|
.911**
|
|
Â
|
(.282)
|
(.274)
|
(.357)
|
(.315)
|
(.282)
|
(.274)
|
(.357)
|
(.315)
|
|
employmentPrefer not to answer
|
.260
|
.584
|
.401
|
1.250***
|
.260
|
.584
|
.401
|
1.249***
|
|
Â
|
(.321)
|
(.317)
|
(.402)
|
(.353)
|
(.321)
|
(.317)
|
(.402)
|
(.353)
|
|
employmentUnemployed
|
1.424
|
-1.013
|
-19.635
|
.922
|
1.424
|
-1.013
|
-20.468
|
.921
|
|
Â
|
(1.040)
|
(1.227)
|
(27464.084)
|
(1.060)
|
(1.040)
|
(1.227)
|
(295.603)
|
(1.060)
|
|
income_log
|
-.377**
|
-.346**
|
-.548***
|
-.483***
|
-.377**
|
-.346**
|
-.548***
|
-.484***
|
|
Â
|
(.130)
|
(.133)
|
(.150)
|
(.142)
|
(.130)
|
(.133)
|
(.149)
|
(.142)
|
|
ladder
|
-.206**
|
-.190**
|
-.187*
|
-.202**
|
-.206**
|
-.190**
|
-.187*
|
-.203**
|
|
Â
|
(.073)
|
(.073)
|
(.085)
|
(.078)
|
(.073)
|
(.073)
|
(.085)
|
(.078)
|
|
PoliticalOrientation
|
-.055
|
-.008
|
-.131
|
-.064
|
-.055
|
-.008
|
-.131
|
-.064
|
|
Â
|
(.070)
|
(.068)
|
(.082)
|
(.071)
|
(.070)
|
(.068)
|
(.082)
|
(.071)
|
|
nonwhite_share:incomeShare_difference
|
.000
|
.002
|
-.004
|
.003
|
Â
|
Â
|
Â
|
Â
|
|
Â
|
(.007)
|
(.007)
|
(.008)
|
(.007)
|
Â
|
Â
|
Â
|
Â
|
|
white_share
|
Â
|
Â
|
Â
|
Â
|
.010
|
-.001
|
-.011
|
.004
|
|
Â
|
Â
|
Â
|
Â
|
Â
|
(.009)
|
(.009)
|
(.010)
|
(.009)
|
|
white_share:incomeShare_difference
|
Â
|
Â
|
Â
|
Â
|
-.000
|
-.002
|
.004
|
-.003
|
|
Â
|
Â
|
Â
|
Â
|
Â
|
(.007)
|
(.007)
|
(.008)
|
(.007)
|
|
AIC
|
649.813
|
667.312
|
529.065
|
634.644
|
649.813
|
667.312
|
529.065
|
634.644
|
|
BIC
|
742.313
|
759.812
|
621.565
|
727.144
|
742.313
|
759.812
|
621.565
|
727.144
|
|
Log Likelihood
|
-302.906
|
-311.656
|
-242.533
|
-295.322
|
-302.906
|
-311.656
|
-242.533
|
-295.322
|
|
Num. obs.
|
495
|
495
|
495
|
495
|
495
|
495
|
495
|
495
|
|
Num. groups: X
|
495
|
495
|
495
|
495
|
495
|
495
|
495
|
495
|
|
Var: X (Intercept)
|
.000
|
.015
|
.000
|
.024
|
.000
|
.015
|
.000
|
.023
|
|
***p < 0.001; **p < 0.01; *p <
0.05
|
Moderator Analysis:
Political Orientation
fairness
summary(m1_1 <- lme4::glmer(fairness ~ PoliticalOrientation*nonwhite_share*incomeShare_difference +(1 | X), cd, family = binomial(link = "logit")))
## boundary (singular) fit: see help('isSingular')
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## fairness ~ PoliticalOrientation * nonwhite_share * incomeShare_difference +
## (1 | X)
## Data: cd
##
## AIC BIC logLik deviance df.resid
## 652.5 690.3 -317.3 634.5 486
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.0896 -0.7827 -0.6244 1.1027 2.5349
##
## Random effects:
## Groups Name Variance Std.Dev.
## X (Intercept) 1.322e-13 3.637e-07
## Number of obs: 495, groups: X, 495
##
## Fixed effects:
## Estimate
## (Intercept) -0.4302977
## PoliticalOrientation 0.0363387
## nonwhite_share 0.0056792
## incomeShare_difference -0.3031909
## PoliticalOrientation:nonwhite_share -0.0041353
## PoliticalOrientation:incomeShare_difference -0.0033735
## nonwhite_share:incomeShare_difference 0.0032078
## PoliticalOrientation:nonwhite_share:incomeShare_difference -0.0006583
## Std. Error z value
## (Intercept) 0.5958138 -0.722
## PoliticalOrientation 0.1479730 0.246
## nonwhite_share 0.0226884 0.250
## incomeShare_difference 0.5066169 -0.598
## PoliticalOrientation:nonwhite_share 0.0056759 -0.729
## PoliticalOrientation:incomeShare_difference 0.1249851 -0.027
## nonwhite_share:incomeShare_difference 0.0173284 0.185
## PoliticalOrientation:nonwhite_share:incomeShare_difference 0.0043372 -0.152
## Pr(>|z|)
## (Intercept) 0.470
## PoliticalOrientation 0.806
## nonwhite_share 0.802
## incomeShare_difference 0.550
## PoliticalOrientation:nonwhite_share 0.466
## PoliticalOrientation:incomeShare_difference 0.978
## nonwhite_share:incomeShare_difference 0.853
## PoliticalOrientation:nonwhite_share:incomeShare_difference 0.879
##
## Correlation of Fixed Effects:
## (Intr) PltclO nnwht_ incmS_ PltO:_ PlO:S_ nn_:S_
## PltclOrnttn -0.929
## nonwhit_shr -0.903 0.841
## incmShr_dff 0.048 -0.056 -0.038
## PltclOrnt:_ 0.835 -0.901 -0.928 0.044
## PltclOrn:S_ -0.056 0.071 0.045 -0.930 -0.057
## nnwht_sh:S_ -0.045 0.053 0.048 -0.924 -0.055 0.862
## PltclO:_:S_ 0.052 -0.066 -0.055 0.849 0.071 -0.916 -0.929
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
plot1_1 <- interact_plot(model = m1_1, pred = nonwhite_share, modx = incomeShare_difference, mod2 = PoliticalOrientation, x.label = "Non white Ratio", y.label = "0 = fair, 1 = unfair", legend.main = "Political Orientation") + ylim(c(0,1))
ggsave(plot = plot1_1, width = 10, height = 8, filename = "Pol_Fairness_nw.jpg")
want to work in
team
summary(m2_1 <- lme4::glmer(work_in_team ~ PoliticalOrientation*nonwhite_share*incomeShare_difference +(1 | X), cd, family = binomial(link = "logit")))
## boundary (singular) fit: see help('isSingular')
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## work_in_team ~ PoliticalOrientation * nonwhite_share * incomeShare_difference +
## (1 | X)
## Data: cd
##
## AIC BIC logLik deviance df.resid
## 673.4 711.2 -327.7 655.4 486
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3636 -0.8831 -0.6552 0.9994 2.3676
##
## Random effects:
## Groups Name Variance Std.Dev.
## X (Intercept) 0 0
## Number of obs: 495, groups: X, 495
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) -0.841850 0.590198
## PoliticalOrientation 0.157621 0.145860
## nonwhite_share 0.029885 0.022272
## incomeShare_difference -0.312726 0.501744
## PoliticalOrientation:nonwhite_share -0.007691 0.005539
## PoliticalOrientation:incomeShare_difference -0.005454 0.123453
## nonwhite_share:incomeShare_difference 0.005680 0.017059
## PoliticalOrientation:nonwhite_share:incomeShare_difference -0.001469 0.004258
## z value Pr(>|z|)
## (Intercept) -1.426 0.154
## PoliticalOrientation 1.081 0.280
## nonwhite_share 1.342 0.180
## incomeShare_difference -0.623 0.533
## PoliticalOrientation:nonwhite_share -1.389 0.165
## PoliticalOrientation:incomeShare_difference -0.044 0.965
## nonwhite_share:incomeShare_difference 0.333 0.739
## PoliticalOrientation:nonwhite_share:incomeShare_difference -0.345 0.730
##
## Correlation of Fixed Effects:
## (Intr) PltclO nnwht_ incmS_ PltO:_ PlO:S_ nn_:S_
## PltclOrnttn -0.930
## nonwhit_shr -0.905 0.843
## incmShr_dff 0.021 -0.026 -0.008
## PltclOrnt:_ 0.838 -0.904 -0.929 0.014
## PltclOrn:S_ -0.027 0.033 0.014 -0.930 -0.020
## nnwht_sh:S_ -0.012 0.019 0.002 -0.926 -0.011 0.863
## PltclO:_:S_ 0.018 -0.026 -0.011 0.851 0.021 -0.918 -0.929
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
plot2_1 <- interact_plot(model = m2_1, pred = nonwhite_share, modx = incomeShare_difference, mod2 = PoliticalOrientation, x.label = "Non white Ratio", y.label = "0 = want to work in team, 1 = do not want to work in team", legend.main = "Political Orientation") + ylim(c(0,1))
ggsave(plot = plot2_1, width = 10, height = 8, filename = "Pol_Want_to_work_nw.jpg")
team work
summary(m3_1 <- lme4::glmer(team_work ~ PoliticalOrientation*nonwhite_share*incomeShare_difference +(1 | X), cd, family = binomial(link = "logit")))
## boundary (singular) fit: see help('isSingular')
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## team_work ~ PoliticalOrientation * nonwhite_share * incomeShare_difference +
## (1 | X)
## Data: cd
##
## AIC BIC logLik deviance df.resid
## 529.7 567.5 -255.8 511.7 486
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.9726 -0.5730 -0.4767 -0.2467 5.4726
##
## Random effects:
## Groups Name Variance Std.Dev.
## X (Intercept) 4e-14 2e-07
## Number of obs: 495, groups: X, 495
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) -2.395034 0.716489
## PoliticalOrientation 0.253311 0.176869
## nonwhite_share 0.065877 0.026244
## incomeShare_difference 0.043031 0.596455
## PoliticalOrientation:nonwhite_share -0.015748 0.006799
## PoliticalOrientation:incomeShare_difference -0.026571 0.146549
## nonwhite_share:incomeShare_difference 0.007171 0.019779
## PoliticalOrientation:nonwhite_share:incomeShare_difference -0.004160 0.005092
## z value Pr(>|z|)
## (Intercept) -3.343 0.00083 ***
## PoliticalOrientation 1.432 0.15209
## nonwhite_share 2.510 0.01207 *
## incomeShare_difference 0.072 0.94249
## PoliticalOrientation:nonwhite_share -2.316 0.02055 *
## PoliticalOrientation:incomeShare_difference -0.181 0.85612
## nonwhite_share:incomeShare_difference 0.363 0.71696
## PoliticalOrientation:nonwhite_share:incomeShare_difference -0.817 0.41400
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PltclO nnwht_ incmS_ PltO:_ PlO:S_ nn_:S_
## PltclOrnttn -0.929
## nonwhit_shr -0.911 0.860
## incmShr_dff 0.044 -0.069 -0.041
## PltclOrnt:_ 0.819 -0.901 -0.924 0.064
## PltclOrn:S_ -0.070 0.112 0.068 -0.930 -0.109
## nnwht_sh:S_ -0.050 0.081 0.066 -0.930 -0.105 0.875
## PltclO:_:S_ 0.077 -0.129 -0.106 0.834 0.173 -0.912 -0.924
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
plot3_1 <- interact_plot(model = m3_1, pred = nonwhite_share, modx = incomeShare_difference, mod2 = PoliticalOrientation, x.label = "Non white Ratio", y.label = "0 = team work well, 1 = team work not well", legend.main = "Political Orientation") + ylim(c(0,1))
ggsave(plot = plot3_1, width = 10, height = 8, filename = "Pol_team_work_nw.jpg")
distrust
summary(m4_1 <- lme4::glmer(distrust ~ PoliticalOrientation*nonwhite_share*incomeShare_difference +(1 | X), cd, family = binomial(link = "logit")))
## boundary (singular) fit: see help('isSingular')
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## distrust ~ PoliticalOrientation * nonwhite_share * incomeShare_difference +
## (1 | X)
## Data: cd
##
## AIC BIC logLik deviance df.resid
## 641.1 679.0 -311.6 623.1 486
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4400 -0.8102 -0.5809 1.1019 3.7479
##
## Random effects:
## Groups Name Variance Std.Dev.
## X (Intercept) 4e-14 2e-07
## Number of obs: 495, groups: X, 495
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) -0.677648 0.600783
## PoliticalOrientation 0.068509 0.151370
## nonwhite_share 0.020112 0.022833
## incomeShare_difference -0.149961 0.510436
## PoliticalOrientation:nonwhite_share -0.006765 0.005862
## PoliticalOrientation:incomeShare_difference -0.048148 0.128714
## nonwhite_share:incomeShare_difference 0.015126 0.017503
## PoliticalOrientation:nonwhite_share:incomeShare_difference -0.004298 0.004547
## z value Pr(>|z|)
## (Intercept) -1.128 0.259
## PoliticalOrientation 0.453 0.651
## nonwhite_share 0.881 0.378
## incomeShare_difference -0.294 0.769
## PoliticalOrientation:nonwhite_share -1.154 0.248
## PoliticalOrientation:incomeShare_difference -0.374 0.708
## nonwhite_share:incomeShare_difference 0.864 0.387
## PoliticalOrientation:nonwhite_share:incomeShare_difference -0.945 0.345
##
## Correlation of Fixed Effects:
## (Intr) PltclO nnwht_ incmS_ PltO:_ PlO:S_ nn_:S_
## PltclOrnttn -0.928
## nonwhit_shr -0.903 0.844
## incmShr_dff 0.050 -0.070 -0.040
## PltclOrnt:_ 0.828 -0.900 -0.926 0.059
## PltclOrn:S_ -0.071 0.103 0.060 -0.928 -0.091
## nnwht_sh:S_ -0.048 0.071 0.051 -0.923 -0.080 0.863
## PltclO:_:S_ 0.068 -0.104 -0.079 0.838 0.125 -0.911 -0.926
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
plot4_1 <- interact_plot(model = m4_1, pred = nonwhite_share, modx = incomeShare_difference, mod2 = PoliticalOrientation, x.label = "Non white Ratio", y.label = "0 = distrust , 1 = no distrust", legend.main = "Political Orientation") + ylim(c(0,1))
ggsave(plot = plot3_1, width = 10, height = 8, filename = "Pol_trust_nw.jpg")
interaction_polorientation <- ggarrange(plot1_1, plot2_1, plot3_1, plot4_1, ncol = 2, nrow = 2, common.legend = T)
ggsave(plot = interaction_polorientation, width = 12, height = 8, filename = "polorientation.jpg")
knitr::include_graphics("polorientation.jpg")

interaction_polorientation <- ggarrange(plot1_2, plot2_2, plot3_2, plot4_2, ncol = 2, nrow = 2, common.legend = T)
ggsave(plot = interaction_polorientation, width = 12, height = 8, filename = "polorientation_w.jpg")
knitr::include_graphics("polorientation_w.jpg")

Moderator Analysis:
SDO
fairness
summary(m1_2 <- lme4::glmer(fairness ~ SDO *nonwhite_share*incomeShare_difference +(1 | X), cd, family = binomial(link = "logit")))
## boundary (singular) fit: see help('isSingular')
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: fairness ~ SDO * nonwhite_share * incomeShare_difference + (1 |
## X)
## Data: cd
##
## AIC BIC logLik deviance df.resid
## 645.6 683.5 -313.8 627.6 486
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5001 -0.7559 -0.6104 1.0671 2.6269
##
## Random effects:
## Groups Name Variance Std.Dev.
## X (Intercept) 0 0
## Number of obs: 495, groups: X, 495
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.784377 1.187427 -2.345 0.0190
## SDO 0.442904 0.205304 2.157 0.0310
## nonwhite_share 0.070738 0.044030 1.607 0.1081
## incomeShare_difference 0.529894 1.032105 0.513 0.6077
## SDO:nonwhite_share -0.014328 0.007664 -1.870 0.0615
## SDO:incomeShare_difference -0.147505 0.175647 -0.840 0.4010
## nonwhite_share:incomeShare_difference -0.005186 0.034086 -0.152 0.8791
## SDO:nonwhite_share:incomeShare_difference 0.000942 0.005860 0.161 0.8723
##
## (Intercept) *
## SDO *
## nonwhite_share
## incomeShare_difference
## SDO:nonwhite_share .
## SDO:incomeShare_difference
## nonwhite_share:incomeShare_difference
## SDO:nonwhite_share:incomeShare_difference
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) SDO nnwht_ incmS_ SDO:n_ SDO:S_ nn_:S_
## SDO -0.982
## nonwhit_shr -0.908 0.894
## incmShr_dff -0.056 0.063 0.070
## SDO:nnwht_s 0.887 -0.906 -0.981 -0.073
## SDO:ncmShr_ 0.063 -0.069 -0.072 -0.983 0.074
## nnwht_sh:S_ 0.057 -0.061 -0.053 -0.932 0.052 0.919
## SDO:nnw_:S_ -0.060 0.062 0.051 0.908 -0.048 -0.928 -0.982
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
plot1_2 <- interact_plot(model = m1_2, pred = nonwhite_share, modx = incomeShare_difference, mod2 = SDO, x.label = "Non white Ratio", y.label = "0 = fair, 1 = unfair", legend.main = "SDO") + ylim(c(0,1))
ggsave(plot = plot1_2, width = 10, height = 8, filename = "SDO_Fairness_nw.jpg")
want to work in
team
summary(m2_2 <- lme4::glmer(work_in_team ~ SDO*nonwhite_share*incomeShare_difference +(1 | X), cd, family = binomial(link = "logit")))
## boundary (singular) fit: see help('isSingular')
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: work_in_team ~ SDO * nonwhite_share * incomeShare_difference +
## (1 | X)
## Data: cd
##
## AIC BIC logLik deviance df.resid
## 669.8 707.7 -325.9 651.8 486
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3521 -0.8730 -0.6299 0.9622 2.6346
##
## Random effects:
## Groups Name Variance Std.Dev.
## X (Intercept) 4.232e-14 2.057e-07
## Number of obs: 495, groups: X, 495
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.193336 1.155193 -1.899 0.0576
## SDO 0.344407 0.199750 1.724 0.0847
## nonwhite_share 0.087012 0.043075 2.020 0.0434
## incomeShare_difference -1.141173 1.014826 -1.125 0.2608
## SDO:nonwhite_share -0.015276 0.007491 -2.039 0.0414
## SDO:incomeShare_difference 0.139742 0.172334 0.811 0.4174
## nonwhite_share:incomeShare_difference 0.040094 0.033703 1.190 0.2342
## SDO:nonwhite_share:incomeShare_difference -0.006982 0.005785 -1.207 0.2275
##
## (Intercept) .
## SDO .
## nonwhite_share *
## incomeShare_difference
## SDO:nonwhite_share *
## SDO:incomeShare_difference
## nonwhite_share:incomeShare_difference
## SDO:nonwhite_share:incomeShare_difference
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) SDO nnwht_ incmS_ SDO:n_ SDO:S_ nn_:S_
## SDO -0.982
## nonwhit_shr -0.908 0.894
## incmShr_dff 0.016 -0.004 0.018
## SDO:nnwht_s 0.888 -0.907 -0.981 -0.025
## SDO:ncmShr_ -0.005 -0.008 -0.024 -0.983 0.032
## nnwht_sh:S_ 0.000 -0.009 -0.018 -0.933 0.021 0.920
## SDO:nnw_:S_ -0.008 0.016 0.020 0.909 -0.022 -0.929 -0.982
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
plot2_2 <- interact_plot(model = m2_2, pred = nonwhite_share, modx = incomeShare_difference, mod2 = SDO, x.label = "Non white Ratio", y.label = "0 = want to work in team, 1 = do not want to work in team", legend.main = "SDO") + ylim(c(0,1))
ggsave(plot = plot2_2, width = 10, height = 8, filename = "SDO_Work_in_team_nw.jpg")
team work
summary(m3_2 <- lme4::glmer(team_work ~ SDO*nonwhite_share*incomeShare_difference +(1 | X), cd, family = binomial(link = "logit")))
## boundary (singular) fit: see help('isSingular')
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: team_work ~ SDO * nonwhite_share * incomeShare_difference + (1 |
## X)
## Data: cd
##
## AIC BIC logLik deviance df.resid
## 523.5 561.4 -252.8 505.5 486
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.1820 -0.5473 -0.4745 -0.2420 7.0232
##
## Random effects:
## Groups Name Variance Std.Dev.
## X (Intercept) 2.687e-14 1.639e-07
## Number of obs: 495, groups: X, 495
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.937992 1.346580 -1.439 0.15010
## SDO 0.091008 0.234950 0.387 0.69850
## nonwhite_share 0.076392 0.049593 1.540 0.12347
## incomeShare_difference -2.229316 1.159495 -1.923 0.05452
## SDO:nonwhite_share -0.012556 0.008802 -1.427 0.15371
## SDO:incomeShare_difference 0.383459 0.198539 1.931 0.05343
## nonwhite_share:incomeShare_difference 0.101918 0.038510 2.647 0.00813
## SDO:nonwhite_share:incomeShare_difference -0.019488 0.006706 -2.906 0.00366
##
## (Intercept)
## SDO
## nonwhite_share
## incomeShare_difference .
## SDO:nonwhite_share
## SDO:incomeShare_difference .
## nonwhite_share:incomeShare_difference **
## SDO:nonwhite_share:incomeShare_difference **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) SDO nnwht_ incmS_ SDO:n_ SDO:S_ nn_:S_
## SDO -0.980
## nonwhit_shr -0.911 0.899
## incmShr_dff 0.109 -0.091 -0.063
## SDO:nnwht_s 0.882 -0.907 -0.979 0.053
## SDO:ncmShr_ -0.093 0.077 0.057 -0.982 -0.050
## nnwht_sh:S_ -0.090 0.081 0.076 -0.933 -0.079 0.920
## SDO:nnw_:S_ 0.081 -0.076 -0.082 0.903 0.092 -0.926 -0.980
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
plot3_2 <- interact_plot(model = m3_2, pred = nonwhite_share, modx = incomeShare_difference, mod2 = SDO, x.label = "Non white Ratio", y.label = "0 = team work well, 1 = team work not well", legend.main = "SDO") + ylim(c(0,1))
ggsave(plot = plot3_2, width = 10, height = 8, filename = "SDO_Team_work_nw.jpg")
trust in
leadership
summary(m4_2 <- lme4::glmer(distrust ~ SDO*nonwhite_share*incomeShare_difference +(1 | X), cd, family = binomial(link = "logit")))
## boundary (singular) fit: see help('isSingular')
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: distrust ~ SDO * nonwhite_share * incomeShare_difference + (1 |
## X)
## Data: cd
##
## AIC BIC logLik deviance df.resid
## 640.7 678.6 -311.4 622.7 486
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4067 -0.7712 -0.5864 1.0606 3.3852
##
## Random effects:
## Groups Name Variance Std.Dev.
## X (Intercept) 0 0
## Number of obs: 495, groups: X, 495
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.850199 1.227719 -2.322 0.0203
## SDO 0.428279 0.210818 2.032 0.0422
## nonwhite_share 0.074767 0.045220 1.653 0.0983
## incomeShare_difference -1.397601 1.072537 -1.303 0.1925
## SDO:nonwhite_share -0.014001 0.007849 -1.784 0.0745
## SDO:incomeShare_difference 0.181591 0.181310 1.002 0.3166
## nonwhite_share:incomeShare_difference 0.066822 0.035448 1.885 0.0594
## SDO:nonwhite_share:incomeShare_difference -0.011611 0.006080 -1.910 0.0562
##
## (Intercept) *
## SDO *
## nonwhite_share .
## incomeShare_difference
## SDO:nonwhite_share .
## SDO:incomeShare_difference
## nonwhite_share:incomeShare_difference .
## SDO:nonwhite_share:incomeShare_difference .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) SDO nnwht_ incmS_ SDO:n_ SDO:S_ nn_:S_
## SDO -0.983
## nonwhit_shr -0.910 0.898
## incmShr_dff 0.071 -0.056 -0.025
## SDO:nnwht_s 0.888 -0.908 -0.982 0.016
## SDO:ncmShr_ -0.058 0.044 0.018 -0.984 -0.010
## nnwht_sh:S_ -0.048 0.038 0.021 -0.934 -0.018 0.923
## SDO:nnw_:S_ 0.038 -0.030 -0.019 0.909 0.019 -0.929 -0.982
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
plot4_2 <- interact_plot(model = m4_2, pred = nonwhite_share, modx = incomeShare_difference, mod2 = SDO, x.label = "Non white Ratio", y.label = "0 = Trust , 1 = distrust", legend.main = "SDO") + ylim(c(0,1))
ggsave(plot = plot4_2, width = 10, height = 8, filename = "SDO_Trust_nw.jpg")
interaction_SDO <- ggarrange(plot1_2, plot2_2, plot3_2, plot4_2, ncol = 2, nrow = 2, common.legend = T)
ggsave(plot = interaction_SDO, width = 12, height = 8, filename = "SDO.jpg")
knitr::include_graphics("SDO.jpg")

interaction_SDO <- ggarrange(plot1_22, plot2_22, plot3_22, plot4_22, ncol = 2, nrow = 2, common.legend = T)
ggsave(plot = interaction_SDO, width = 12, height = 8, filename = "SDO_w.jpg")
knitr::include_graphics("SDO_w.jpg")

## Table nw
M1_2 = Fairness nw (DV), M2_2 = Want to work in team nw(DV), M3_2 = Team
work nw (DV), M4_2 = Distrust nw (DV)
|
Â
|
Model 1
|
Model 2
|
Model 3
|
Model 4
|
|
(Intercept)
|
-2.784*
|
-2.193
|
-1.938
|
-2.850*
|
|
Â
|
(1.187)
|
(1.155)
|
(1.347)
|
(1.228)
|
|
SDO
|
.443*
|
.344
|
.091
|
.428*
|
|
Â
|
(.205)
|
(.200)
|
(.235)
|
(.211)
|
|
nonwhite_share
|
.071
|
.087*
|
.076
|
.075
|
|
Â
|
(.044)
|
(.043)
|
(.050)
|
(.045)
|
|
incomeShare_difference
|
.530
|
-1.141
|
-2.229
|
-1.398
|
|
Â
|
(1.032)
|
(1.015)
|
(1.159)
|
(1.073)
|
|
SDO:nonwhite_share
|
-.014
|
-.015*
|
-.013
|
-.014
|
|
Â
|
(.008)
|
(.007)
|
(.009)
|
(.008)
|
|
SDO:incomeShare_difference
|
-.148
|
.140
|
.383
|
.182
|
|
Â
|
(.176)
|
(.172)
|
(.199)
|
(.181)
|
|
nonwhite_share:incomeShare_difference
|
-.005
|
.040
|
.102**
|
.067
|
|
Â
|
(.034)
|
(.034)
|
(.039)
|
(.035)
|
|
SDO:nonwhite_share:incomeShare_difference
|
.001
|
-.007
|
-.019**
|
-.012
|
|
Â
|
(.006)
|
(.006)
|
(.007)
|
(.006)
|
|
AIC
|
645.641
|
669.815
|
523.525
|
640.723
|
|
BIC
|
683.482
|
707.656
|
561.366
|
678.564
|
|
Log Likelihood
|
-313.820
|
-325.908
|
-252.763
|
-311.361
|
|
Num. obs.
|
495
|
495
|
495
|
495
|
|
Num. groups: X
|
495
|
495
|
495
|
495
|
|
Var: X (Intercept)
|
.000
|
.000
|
.000
|
.000
|
|
***p < 0.001; **p < 0.01; *p <
0.05
|
Moderator Analysis:
Race
fairness
summary(m1_44 <- lme4::glmer(fairness ~ ethnic *nonwhite_share*incomeShare_difference +(1 | X), cd, family = binomial(link = "logit")))
## boundary (singular) fit: see help('isSingular')
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: fairness ~ ethnic * nonwhite_share * incomeShare_difference +
## (1 | X)
## Data: cd
##
## AIC BIC logLik deviance df.resid
## 649.0 686.8 -315.5 631.0 486
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2076 -0.7471 -0.6211 1.0582 2.0223
##
## Random effects:
## Groups Name Variance Std.Dev.
## X (Intercept) 1.054e-12 1.027e-06
## Number of obs: 495, groups: X, 495
##
## Fixed effects:
## Estimate Std. Error z value
## (Intercept) -0.149467 0.262074 -0.570
## ethnic1 -0.535749 0.491108 -1.091
## nonwhite_share -0.010842 0.009987 -1.086
## incomeShare_difference -0.399857 0.216777 -1.845
## ethnic1:nonwhite_share 0.004979 0.018778 0.265
## ethnic1:incomeShare_difference 0.407509 0.432229 0.943
## nonwhite_share:incomeShare_difference 0.002955 0.007457 0.396
## ethnic1:nonwhite_share:incomeShare_difference -0.010477 0.014787 -0.709
## Pr(>|z|)
## (Intercept) 0.5685
## ethnic1 0.2753
## nonwhite_share 0.2776
## incomeShare_difference 0.0651 .
## ethnic1:nonwhite_share 0.7909
## ethnic1:incomeShare_difference 0.3458
## nonwhite_share:incomeShare_difference 0.6919
## ethnic1:nonwhite_share:incomeShare_difference 0.4786
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ethnc1 nnwht_ incmS_ eth1:_ et1:S_ nn_:S_
## ethnic1 -0.534
## nonwhit_shr -0.903 0.482
## incmShr_dff 0.052 -0.028 -0.038
## ethnc1:nnw_ 0.480 -0.902 -0.532 0.020
## ethnc1:ncS_ -0.026 -0.028 0.019 -0.502 0.004
## nnwht_sh:S_ -0.045 0.024 0.044 -0.922 -0.023 0.463
## ethnc1:_:S_ 0.023 0.003 -0.022 0.465 0.035 -0.924 -0.504
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
plot1_44 <- interactions::interact_plot(m1_44, pred = incomeShare_difference, modx = ethnic, x.label = "Non white Ratio", y.label = "0 = fair, 1 = unfair", legend.main = "own ethnicity") + ylim(c(0,1), (legend = "0 = White, 1 = Non white"))
ggsave(plot = plot1_44, width = 10, height = 8, filename = "Race_fairness_nw.jpg")
want to work in
team
summary(m2_4 <- lme4::glmer(work_in_team ~ ethnic*nonwhite_share*incomeShare_difference +(1 | X), cd, family = binomial(link = "logit")))
## boundary (singular) fit: see help('isSingular')
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: work_in_team ~ ethnic * nonwhite_share * incomeShare_difference +
## (1 | X)
## Data: cd
##
## AIC BIC logLik deviance df.resid
## 676.1 713.9 -329.1 658.1 486
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3215 -0.9049 -0.6685 0.9845 1.7455
##
## Random effects:
## Groups Name Variance Std.Dev.
## X (Intercept) 1.048e-14 1.024e-07
## Number of obs: 495, groups: X, 495
##
## Fixed effects:
## Estimate Std. Error z value
## (Intercept) -0.2023802 0.2586543 -0.782
## ethnic1 -0.1498902 0.4736060 -0.316
## nonwhite_share 0.0003096 0.0097882 0.032
## incomeShare_difference -0.3246762 0.2133824 -1.522
## ethnic1:nonwhite_share 0.0024577 0.0179001 0.137
## ethnic1:incomeShare_difference -0.0348817 0.4217334 -0.083
## nonwhite_share:incomeShare_difference 0.0006419 0.0073063 0.088
## ethnic1:nonwhite_share:incomeShare_difference -0.0003778 0.0143409 -0.026
## Pr(>|z|)
## (Intercept) 0.434
## ethnic1 0.752
## nonwhite_share 0.975
## incomeShare_difference 0.128
## ethnic1:nonwhite_share 0.891
## ethnic1:incomeShare_difference 0.934
## nonwhite_share:incomeShare_difference 0.930
## ethnic1:nonwhite_share:incomeShare_difference 0.979
##
## Correlation of Fixed Effects:
## (Intr) ethnc1 nnwht_ incmS_ eth1:_ et1:S_ nn_:S_
## ethnic1 -0.546
## nonwhit_shr -0.904 0.494
## incmShr_dff 0.023 -0.013 -0.007
## ethnc1:nnw_ 0.494 -0.904 -0.547 0.004
## ethnc1:ncS_ -0.012 -0.026 0.004 -0.506 0.016
## nnwht_sh:S_ -0.012 0.006 -0.002 -0.923 0.001 0.467
## ethnc1:_:S_ 0.006 0.017 0.001 0.470 -0.002 -0.926 -0.509
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
plot2_4 <- interactions::interact_plot(m2_4, pred = incomeShare_difference, modx = ethnic, x.label = "Non white Ratio", y.label = "0 = want to work in team, 1 = do not want to work in team", legend.main = "ethnic") + ylim(c(0,1),(legend = "0 = White, 1 = Non white"))
team work
summary(m3_4 <- lme4::glmer(team_work ~ ethnic*nonwhite_share*incomeShare_difference +(1 | X), cd, family = binomial(link = "logit")))
## boundary (singular) fit: see help('isSingular')
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: team_work ~ ethnic * nonwhite_share * incomeShare_difference +
## (1 | X)
## Data: cd
##
## AIC BIC logLik deviance df.resid
## 540.5 578.3 -261.2 522.5 486
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.8975 -0.5690 -0.4967 -0.3897 2.7327
##
## Random effects:
## Groups Name Variance Std.Dev.
## X (Intercept) 2.414e-17 4.913e-09
## Number of obs: 495, groups: X, 495
##
## Fixed effects:
## Estimate Std. Error z value
## (Intercept) -1.500647 0.317318 -4.729
## ethnic1 0.026260 0.564096 0.047
## nonwhite_share 0.009679 0.011805 0.820
## incomeShare_difference -0.136890 0.258194 -0.530
## ethnic1:nonwhite_share 0.003419 0.020758 0.165
## ethnic1:incomeShare_difference 0.135096 0.492268 0.274
## nonwhite_share:incomeShare_difference -0.005549 0.008744 -0.635
## ethnic1:nonwhite_share:incomeShare_difference 0.002329 0.016367 0.142
## Pr(>|z|)
## (Intercept) 2.25e-06 ***
## ethnic1 0.963
## nonwhite_share 0.412
## incomeShare_difference 0.596
## ethnic1:nonwhite_share 0.869
## ethnic1:incomeShare_difference 0.784
## nonwhite_share:incomeShare_difference 0.526
## ethnic1:nonwhite_share:incomeShare_difference 0.887
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ethnc1 nnwht_ incmS_ eth1:_ et1:S_ nn_:S_
## ethnic1 -0.563
## nonwhit_shr -0.908 0.510
## incmShr_dff 0.147 -0.082 -0.127
## ethnc1:nnw_ 0.516 -0.910 -0.569 0.072
## ethnc1:ncS_ -0.077 -0.006 0.067 -0.524 -0.010
## nnwht_sh:S_ -0.143 0.081 0.157 -0.927 -0.089 0.486
## ethnc1:_:S_ 0.077 -0.012 -0.084 0.495 0.043 -0.931 -0.534
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
plot3_4 <- interactions::interact_plot(m3_4, pred = incomeShare_difference, modx = ethnic, x.label = "Non white Ratio", y.label = "0 = team work well, 1 = team work not well", legend.main = "ethnic") + ylim(c(0,1), (legend = "0 = White, 1 = Non white"))
distrust
summary(m4_4 <- lme4::glmer(distrust ~ ethnic*nonwhite_share*incomeShare_difference +(1 | X), cd, family = binomial(link = "logit")))
## boundary (singular) fit: see help('isSingular')
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: distrust ~ ethnic * nonwhite_share * incomeShare_difference +
## (1 | X)
## Data: cd
##
## AIC BIC logLik deviance df.resid
## 647.3 685.1 -314.6 629.3 486
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2281 -0.7377 -0.5948 1.0908 2.1482
##
## Random effects:
## Groups Name Variance Std.Dev.
## X (Intercept) 4e-14 2e-07
## Number of obs: 495, groups: X, 495
##
## Fixed effects:
## Estimate Std. Error z value
## (Intercept) -0.371885 0.268341 -1.386
## ethnic1 -0.306616 0.486597 -0.630
## nonwhite_share -0.004681 0.010208 -0.459
## incomeShare_difference -0.472117 0.223626 -2.111
## ethnic1:nonwhite_share 0.006489 0.018327 0.354
## ethnic1:incomeShare_difference 0.515371 0.427840 1.205
## nonwhite_share:incomeShare_difference 0.002102 0.007703 0.273
## ethnic1:nonwhite_share:incomeShare_difference -0.004671 0.014516 -0.322
## Pr(>|z|)
## (Intercept) 0.1658
## ethnic1 0.5286
## nonwhite_share 0.6465
## incomeShare_difference 0.0348 *
## ethnic1:nonwhite_share 0.7233
## ethnic1:incomeShare_difference 0.2284
## nonwhite_share:incomeShare_difference 0.7850
## ethnic1:nonwhite_share:incomeShare_difference 0.7476
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ethnc1 nnwht_ incmS_ eth1:_ et1:S_ nn_:S_
## ethnic1 -0.551
## nonwhit_shr -0.903 0.498
## incmShr_dff 0.097 -0.054 -0.076
## ethnc1:nnw_ 0.503 -0.905 -0.557 0.042
## ethnc1:ncS_ -0.051 -0.046 0.040 -0.523 0.028
## nnwht_sh:S_ -0.088 0.048 0.086 -0.922 -0.048 0.482
## ethnc1:_:S_ 0.047 0.031 -0.046 0.489 -0.009 -0.926 -0.531
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
plot4_4 <- interactions::interact_plot(m4_4, pred = incomeShare_difference, modx = ethnic, x.label = "Non white Ratio", y.label = "0 = distrust , 1 = no distrust", legend.main = "ethnic") + ylim(c(0,1), (legend = "0 = White, 1 = Non white"))
interaction_ethnic <- ggarrange(plot1_44
, plot2_4, plot3_4, plot4_4, ncol = 2, nrow = 2, common.legend = T)
ggsave(plot = interaction_ethnic, width = 10, height = 8, filename = "ethnic.jpg")
knitr::include_graphics("ethnic.jpg")
